Triple
T9108542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catawba language |
E218536
|
entity |
| Predicate | hasLoanLanguageInfluenceFrom |
P23173
|
FINISHED |
| Object | English language |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: English language | Statement: [Catawba language, hasLoanLanguageInfluenceFrom, English language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoanLanguageInfluenceFrom Context triple: [Catawba language, hasLoanLanguageInfluenceFrom, English language]
-
A.
languageInfluence
chosen
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
B.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
C.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
-
D.
shareLanguageInfluence
Indicates that two entities affect or shape each other’s language use, development, or characteristics through mutual or shared influence.
-
E.
influencedLanguageFamily
Indicates that one language family has had a significant impact on the development, structure, or usage of another language family.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca83db7448819090d0a5de842ef2ac |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca57543448190829853c31e05dd8c |
completed | April 1, 2026, 4:56 a.m. |
| PD | Predicate disambiguation | batch_69cc65fe5be081909d4470d6317b14a6 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:16 p.m.